An interactive Formula 1 telemetry dashboard built with Python that visualizes race data and uses machine learning to predict lap performance.
This project fetches real telemetry data and displays interactive charts to analyze driver performance, race pace, and tyre strategies.
📊 Telemetry Visualization
- Speed trace across the track
- Lap time comparisons
- Tyre strategy analysis
🧠 AI Lap Time Prediction
- Machine learning model predicts lap performance using telemetry data
- Uses features such as speed, throttle, and brake usage
🏁 Driver Performance Analysis
- Compare drivers across laps
- Analyze race pace trends
🗄️ Telemetry Data Storage
- Data stored in a local SQLite database
📈 Interactive Charts
- Built using Python visualization libraries
Python FastF1 API Pandas Scikit-learn SQLite Plotly / Dash Flask
f1-telemetry-dashboard │ ├── charts │ ├── lap_times.py │ ├── speed_trace.py │ └── tyre_strategy.py │ ├── data │ └── f1_telemetry.db │ ├── sql │ └── database.py │ ├── utils │ └── fetcher.py │ ├── ai_model.py ├── main.py ├── requirements.txt └── README.md
This project includes a Random Forest regression model that predicts lap time using telemetry data.
Features used by the model:
- Average Speed
- Maximum Speed
- Throttle Usage
- Brake Usage
The model is trained using historical telemetry extracted from race sessions.
Clone the repository
git clone https://github.com/sanjam3712/f1-telementry-dashboard.git
cd f1-telementry-dashboardInstall dependencies
pip install -r requirements.txtRun the dashboard
python main.pyThe dashboard allows users to explore:
• Driver lap time comparisons • Speed traces throughout the circuit • Tyre degradation and strategy insights • AI-predicted lap performance
Real-time race telemetry integration
Overtake probability prediction
Driver performance ranking using machine learning
Race strategy simulator
Live race analytics dashboard
Sanjam Bedi
Engineering Student Electronics and Computer Engineering Motorsport Analytics Enthusiast